Constructing a Multivalued Representation for View Synthesis

A fundamental problem in computer vision and graphics is that of arbitrary view synthesis for static 3-D scenes, whereby a user-specified viewpoint of the given scene may be created directly from a representation. We propose a novel compact representation for this purpose called the multivalued representation (MVR). Starting with an image sequence captured by a moving camera undergoing either unknown planar translation or orbital motion, a MVR is derived for each preselected reference frame, and may then be used to synthesize arbitrary views of the scene. The representation itself is comprised of multiple depth and intensity levels in which the k-th level consists of points occluded by exactly k surfaces. To build a MVR with respect to a particular reference frame, dense depth maps are first computed for all the neighboring frames of the reference frame. The depth maps are then combined together into a single map, where points are organized by occlusions rather than by coherent affine motions. This grouping facilitates an automatic process to determine the number of levels and helps to reduce the artifacts caused by occlusions in the scene. An iterative multiframe algorithm is presented for dense depth estimation that both handles low-contrast regions and produces piecewise smooth depth maps. Reconstructed views as well as arbitrary flyarounds of real scenes are presented to demonstrate the effectiveness of the approach.     

Coarse‐to‐Fine Isometric Shape Correspondence by Tracking Symmetric Flips

We address the symmetric flip problem that is inherent to multi‐resolution isometric shape matching algorithms. To this effect, we extend our previous work which handles the dense isometric correspondence problem in the original 3D Euclidean space via coarse‐to‐fine combinatorial matching. The key idea is based on keeping track of all optimal solutions, which may be more than one due to symmetry especially at coarse levels, throughout denser levels of the shape matching process. We compare the resulting dense correspondence algorithm with state‐of‐the‐art techniques over several 3D shape benchmark datasets. The experiments show that our method, which is fast and scalable, is performance‐wise better than or on a par with the best performant algorithms existing in the literature for isometric (or nearly isometric) shape correspondence. Our key idea of tracking symmetric flips can be considered as a meta‐approach that can be applied to other multi‐resolution shape matching algorithms, as we also demonstrate by experiments.     

Conformational Heterogeneity and Frustration of the Tumor Suppressor p53 as Tuned by Punctual Mutations

The conformational heterogeneity of the p53 tumor suppressor, the wild-type (p53wt) and mutated forms, was investigated by a computational approach, including the modeling and all atoms of the molecular dynamics (MD) simulations. Four different punctual mutations (p53R175H, p53R248Q, p53R273H, and p53R282W) which are known to affect the DNA binding and belong to the most frequent hot-spot mutations in human cancers, were taken into consideration. The MD trajectories of the wild-type and mutated p53 forms were analyzed by essential dynamics to extract the relevant collective motions and by the frustration method to evaluate the degeneracy of the energy landscape. We found that p53 is characterized by wide collective motions and its energy landscape exhibits a rather high frustration level, especially in the regions involved in the binding to physiological ligands. Punctual mutations give rise to a modulation of both the collective motions and the frustration of p53, with different effects depending on the mutation. The regions of p53wt and of the mutated forms characterized by a high frustration level are also largely involved in the collective motions. Such a correlation is discussed also in connection with the intrinsic disordered character of p53 and with its central functional role.     

基于多尺度耦合的密集残差网络红外图像增强

为了提升非制冷红外热像仪的图像质量,满足低对比度弱小区域的观瞄与锁定的需求,提出了一种基于多尺度密集残差网络的红外图像超分辨重建模型,该模型的基本框架是通过级联多个残差特征进行学习,以粗到细的方式重建高分辨率图像。首先提出一种多尺度跨域融合模块,通过对不同感受野的分支结果进行融合,不仅可以融合不同感受野的互补信息,还可有助于提升梯度收敛和特征传输;然后叠加多个跨域融合模块,并采用残差特征学习进行优化,最终学习出高分辨率细节信息。仿真实验结果表明,所提出的超分辨模型能够较好的超分辨重建效果,在微弱结构保持和点目标保持上的性能也更加突出。所提的模型已经在海思嵌入式深度学习平台上实现了高质量的红外增强,具有较高的工程应用价值。     

基于轻量级密集残差网络的水下图像增强

深度卷积神经网络是水下图像增强的主要方法之一,但其过高的内存消耗和计算需求阻碍了在实际应用中的部署。为此,提出一种轻量级的密集残差卷积神经网络(dense residual convolutional neural networks, DRCNN)用于水下图像增强。为降低计算成本,DRCNN采用深度可分离卷积提取高级特征;通过密集连接和残差学习促进不同通道之间的信息交互,提高模型表征能力;将输入的退化图像与中间特征图融合,保留图像全局相似性,同时防止模型梯度消失。实验结果证明DRCNN能有效提高水下图像质量,较于现有算法,DRCNN参数量减少了85%,PSNR、SSIM值分别提高了3%、2%,测试速度提高了3%。DRCNN使用更少的参数实现了更好的性能,利于在低资源设备的实时场景中应用。     

煤泥燃烧过程中汞的迁移行为研究

采用在线汞测试方法,以山西省低热值煤电厂中掺烧的煤泥为研究对象,利用实验室小型流化床,研究煤泥中汞的热转化行为差异及共性特征、影响煤泥热转化过程中汞迁移的关键因素,以揭示煤泥热转化过程中汞污染物的迁移机理.结果表明,同一种煤泥,相同气氛,800、900、1000℃下,燃烧温度对煤泥中的汞的释放比例没有变化;相同温度,汞...     

Parallel Gauss-Jordan elimination for the solution of dense linear systems

Any factorization/back substitution scheme for the solution of linear systems consists of two phases which are different in nature, and hence may be inefficient for parallel implementation on a single computational network. The Gauss-Jordan elimination scheme unifies the nature of the two phases of the solution process and thus seems to be more suitable for parallel architectures, especially if reconfiguration of the communication pattern is not permitted. In this communication, a computational network for the Gauss-Jordan algorithm is presented. This network compares favorably with optimal implementations of the Gauss elimination/back substitution algorithm.     

What do students do in a F2F CSCL classroom? The optimization of multiple communications modes

This exploratory study analyzes how students use different communication modes to share information, negotiate meaning and construct knowledge in the process of doing a group learning activity in a Primary Grade 5 blended learning environment in Singapore. Small groups of students interacted face-to-face over a computer-mediated communication (CMC) technology called Group Scribbles (GS) to jointly complete a learning task. The lesson designers attempted to optimize the use of CMC technology and face-to-face (F2F) discussion in students’ collaborative learning, with the aim of harnessing the specific features of each medium. Building on notions from communication studies and from interaction analysis, we observed the construction and evolution of the interactions through analyzing the artifacts that were produced by a group of students – in verbal talk, gestures, and sketches drawn and text inscribed in GS. F2F and GS interactions intertwined to support collaborative learning. The findings from this study could inform design aspects concerning integrating and reinforcing the strengths of both communication modes when introducing computer-assisted collaborative learning (CSCL) in a F2F classroom.     

水平井双管注汽工艺技术

水平井双管注汽工艺技术采用内外管方式分别对水平井前端和末端注汽.井口配套工具采用双四通、双悬挂器,同时应用等干度分配器实现双管柱内的蒸汽流量控制及等干度分配,隔热方式为氮气隔热,经现场试验取得良好效果。双管注汽技术的研制成功表明该技术能够在井口实现注汽量的动态调整.使注汽利用率得到提高,有效改善了水平井的开发效果。     

稠油精细注汽配套技术研究及应用

曙光油田稠油、超稠油油藏已经大规模开发,为了提高该类油藏的最终采收率,提出并实施了稠油精细注汽配套技术。该配套技术于2006年在曙光油田累计实施应用了111井次,措施成功率88.3%,平均单井增产原油317.1m~3,取得了较好的应用效果。